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Archive of posts filed under the Political Science category.

Don’t Hate Undecided Voters

This post is by Clay Campaigne, not Andrew. (It says ‘posted by Phil’, and that’s technically true, but I’m just a conduit for Clay here).  This is copied from Clay’s blog, which may have comments of its own so you might want to read it there too. Politics has taken on particular vitriol in recent […]

Prediction markets and election forecasts

Zev Berger writes: The question sounds snarky, but it’s not meant in that vein. It’s instructive to hear how modelers understand the predictions of their models, which is something I am still trying to think through. Your model has the chance of Biden being elected at 0.95. Predictit has Biden at 0.60. Given the spread, […]

Reverse-engineering the problematic tail behavior of the Fivethirtyeight presidential election forecast

We’ve been writing a bit about some odd tail behavior in the Fivethirtyeight election forecast, for example that it was giving Joe Biden a 3% chance of winning Alabama (which seemed high), it was displaying Trump winning California as in “the range of scenarios our model thinks is possible” (which didn’t seem right), and it […]

Merlin did some analysis of possible electoral effects of rejections of vote-by-mail ballots . . .

Elliott writes: Postal voting could put America’s Democrats at a disadvantage: Rejection rates for absentee ballots have fallen since 2016, but are higher for non-whites than whites The final impact of a surge in postal voting will not be known until weeks after the election. Yet North Carolina, a closely contested state, releases detailed data […]

“Election Forecasting: How We Succeeded Brilliantly, Failed Miserably, or Landed Somewhere in Between”

I agreed to give a talk in December for Jared, and this is what I came up with: Election Forecasting: How We Succeeded Brilliantly, Failed Miserably, or Landed Somewhere in Between Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University Several months before the election we worked with The Economist magazine to […]

Presidents as saviors vs. presidents as being hired to do a job

There’s been a lot of talk about how if Biden is elected president it will be a kind of relief, a return to problem solving and dialing down of tension. This is different from Obama, who so famously inspired all that hope, and it made me think about characterizing other modern presidents in this way: […]

Estimated “house effects” (biases of pre-election surveys from different pollsters) and here’s why you have to be careful not to overinterpret them:

Elliott provides the above estimates from our model. As we’ve discussed, as part of our fitting procedure we estimate various biases, capturing in different ways the fact that surveys are not actually random samples of voters from an “urn.” One of these biases is the “house effect.” In our model, everything’s on the logit scale, […]

Whassup with the dots on our graph?

The above is from our Economist election forecast. Someone pointed to me that our estimate is lower than all the dots in October. Why is that? I can come up with some guesses, but it’s surprising that the line is below all the dots. Merlin replied: That happened a bunch of times before as well. […]

Pre-register post-election analyses?

David Randall writes: I [Randall] have written an article on how we need to (in effect) pre-register the election—preregister the methods we will use to analyze the voting, with an eye to determining if there is voter fraud. I have a horrible feeling we’re headed to civil war, and there’s nothing that can be done […]

Between-state correlations and weird conditional forecasts: the correlation depends on where you are in the distribution

Yup, here’s more on the topic, and this post won’t be the last, either . . . Jed Grabman writes: I was intrigued by the observations you made this summer about FiveThirtyEight’s handling of between-state correlations. I spent quite a bit of time looking into the topic and came to the following conclusions. In order […]

Calibration problem in tails of our election forecast

Following up on the last paragraph of this discussion, Elliott looked at the calibration of our state-level election forecasts, fitting our model retroactively to data from the 2008, 2012, and 2016 presidential elections. The plot above shows the point prediction and election outcome for the 50 states in each election, showing in red the states […]

We are stat professors with the American Statistical Association, and we’re thrilled to talk to you about the statistics behind voting. Ask us anything!

It’s happening at 11am today on Reddit. It’s a real privilege to do this with Mary Gray, who was so nice to me back when I took a class at American University several decades ago.

More on martingale property of probabilistic forecasts and some other issues with our election model

Edward Yu writes: I’m wondering if you’ve seen Nassim Taleb’s article arguing that we should price election forecasts as binary options. You seem to be generally fine with this approach, as when Nate Silver asked your colleague: On the off-chance our respective employers would allow it, which they almost certainly wouldn’t in my case, could […]

Response to a question about a reference in one of our papers

Tushar Sunkum writes: I like this particular study that you did [with Jeff Fagan and Alex Kiss] on racial profiling. However, I believe that you misrepresented one of the sources on the paper. You state, “For example, two surveys with nationwide probability samples, completed in 1999 and in 2002, showed that African-Americans were far more […]

Social science and the replication crisis (my talk this Thurs 8 Oct)

My talk at the WZB Berlin Social Science Center 3pm (Central European Time): Social science and the replication crisis The replication crisis is typically discussed in the context of particular silly claims, or in terms of the sociology of science, or with regard to controversies in statistical practice. Here we discuss the content of unreplicated […]

Quino y Mafalda

Obit by Harrison Smith, full of stories: She was a wise and idealistic young girl, a cartoon kid with a ball of black frizz for hair, a passionate hatred of soup and a name, Mafalda, inspired by a failed home appliance brand. Although her creator, a cartoonist known as Quino, drew her regularly for just […]

How to think about extremely unlikely events (such as Biden winning Alabama, Trump winning California, or Biden winning Ohio but losing the election)?

This post is by Elliott Morris, Merlin Heidemanns, and Andrew Gelman. We have written a lot about both our presidential election model at the Economist and Fivethirtyeight’s since they both launched in the summer. We even wrote a journal article (with Jessica Hullman and Chris Wlezien) about what we can learn from them about Bayseian […]

Information, incentives, and goals in election forecasts

Jessica Hullman, Christopher Wlezien, and Elliott Morris and I write: Presidential elections can be forecast using information from political and economic conditions, polls, and a statistical model of changes in public opinion over time. However, these “knowns” about how to make a good presidential election forecast come with many unknowns due to the challenges of […]

Political polarization of professions

Seeing this newspaper article, “In an outraged Louisville, a Police Force in Crisis,” made me think of this discussion from a few years ago. What happened was that a group of psychology researchers wrote an article, “Political Diversity Will Improve Social Psychological Science,” arguing that the field of social psychology would benefit from the inclusion […]

Election Scenario Explorer using Economist Election Model

Ric Fernholz writes: I wanted to tell you about a new website I built together with my brother Dan. The 2020 Election Scenario Explorer allows you to explore how electoral outcomes in individual states influence the national election outlook using data from your election model. The map and tables on our site reveal some interesting […]